In recent years, deep learning has dominated and revolutionized the field of face recognition so that current face recognition surveys and reviews are full of deep learning methods.
These algorithms have advanced face recognition to a level that traditional methods can no longer reach [1]}.
There are two main research directions in the academic community that have tried to improve the performance of neural networks, especially in unconstrained face recognition environments: the engineering of new network topologies and the definition of new loss functions.
Early versions of deep face recognition systems were using only a few convolutional layers, e.g., the VGGFace network [2]} used 13 such layers.
One of the most relevant contributions in terms of network topologies is the Residual Network [3]}, which introduced residual connections between layers that allow training much deeper network structures than it was able before, the most common topologies have 18 to 152 layers.
Another architecture is the Squeeze and Excitation network [4]}, which integrates a special block into current network architectures that allows for automatic weighting of individual convolution channels.
Additionally, so-called lightweight network architectures have recently been developed.
One of these is the MobileNet [5]}, which uses depth-wise separable convolutions and neural architectural search to lead to a considerable reduction of parameters and, therewith, reduced the computing requirements compared to other networks with similar depth.
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